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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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133 records · Page 8

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NASA NTRS1993

Simulation of summertime ozone over North America

The concentrations of O3 and its precursors over North America are simulated for three summer months with a 3D, continental-scale photochemical model using meteorological input from the Goddard Institute for Space Studies (GISS) GCM. The model has 4 x 5 deg grid resolution and represents nonlinear chemistry in urban and industrial plumes with a subgrid nested scheme. Simulated median afternoon O3 concentrations at rural U.S. sites are within 5 ppb of observations in most cases, except in the south central U.S., where concentrations are overpredicted by 15-20 ppb. The model captures successfully the development of regional high-O3 episodes over the northeastern United States on the back side of weak, warm, stagnant anticyclones. Simulated concentrations of CO and nonmethane hydrocarbons are generally in good agreement with observations, concentrations of NO(x) are underpredicted by 10-30 percent, and concentrations of PANs are overpredicted by a factor of 2 to 3. The overprediction of PANs is attributed to flaws in the photochemical mechanism, including excessive production from oxidation of isoprene, and may also reflect an underestimate of PANs deposition. Subgrid nonlinear chemistry as captured by the nested plumes scheme decreases the net O3 production computed in the U.S. boundary layer by 8 percent on average.

Jacob, Daniel J.↗

NASA NTRS2002

Flame and Soot Boundaries of Laminar Jet Diffusion Flames

The shapes (flame-sheet and luminous-flame boundaries) or steady weakly buoyant round hydrocarbon-fueled laminar-jet diffusion flames in still and coflowing air were studied both experimentally and theoretically. Flame-sheet shapes were measured from photographs using a CH optical filter to distinguish flame-sheet boundaries in the presence of blue CO2 and OH emissions and yellow continuum radiation from soot. Present experimental conditions included acetylene-, methane-, propane-, and ethylene-fueled flames having initial reactant temperatures of 300 K. ambient pressures of 4-50 kPa, jet-exit Reynolds numbers of 3-54, initial air/fuel velocity ratios of 0-9, and luminous flame lengths of 5-55 mm; earlier measurements for propylene- and 1,3-butadiene-fueled flames for similar conditions were considered as well. Nonbuoyant flames in still air were observed at microgravity conditions; essentially nonbuoyant flames in coflowing air were observed at small pressures to control effects of buoyancy. Predictions of luminous flame boundaries from soot luminosity were limited to laminar smoke-point conditions, whereas predictions of flame-sheet boundaries ranged from soot-free to smoke-point conditions. Flame-shape predictions were based on simplified analyses using the boundary-layer approximations along with empirical parameters to distinguish flame-sheet and luminous-flame (at the laminar smoke point) boundaries. The comparison between measurements and predictions was remarkably good and showed that both flame-sheet and luminous-flame lengths are primarily controlled by fuel flow rates with lengths in coflowing air approaching 2/3 of the lengths in still air as coflowing air velocities are increased. Finally, luminous flame lengths at laminar smoke-point conditions were roughly twice as long as flame-sheet lengths at comparable conditions because of the presence of luminous soot particles in the fuel-lean region of the flames.

Xu, F.↗

DOE OSTI2019

Graphene-polyelectrolyte multilayer membranes with tunable structure and internal charge

One great advantage of graphene-polyelectrolyte multilayer (GPM) membranes is their tunable structure and internal charge for improved separation performance. In this study, we synthesized GO-dominant GPM membrane with internal negatively-charged domains, polyethyleneimine (PEI)-dominant GPM membrane with internal positively-charged domains and charge-balanced dense/loose GPM membranes by simply adjusting the ionic strength and pH of the GO and PEI solutions used in layer-by-layer membrane synthesis. A combined system of quartz crystal microbalance with dissipation (QCM-D) and ellipsometry was used to analyze the mass deposition, film thickness, and layer density of the GPM membranes. The performance of the GPM membranes were compared in terms of both permeability and selectivity to determine the optimal membrane structure and synthesis strategy. One effective strategy to improve the GPM membrane permeability-selectivity tradeoff is to assemble charge-balanced dense membranes under weak electrostatic interactions. This balanced membrane exhibits the highest MgCl 2 selectivity (~86%). Another effective strategy for improved cation removal is to create PEI-dominant membranes that provide internal positively-charged barrier to enhance cation selectivity without sacrificing water permeability. Finally, these findings shine lights on the development of a systematic approach to push the boundary of permeability-selectivity tradeoff for GPM membranes.

42 ENGINEERING↗

DOE OSTI2024

TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the first ARM Mobile Facility (AMF1) to Houston, Texas for the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign. The TRACER campaign was conducted from October 1, 2021 to September 30, 2022, with an intensive operational period (IOP) from June 1 to September 30, 2022. To investigate the life cycles of convective cells in the polluted and humid urban environment of Houston, ARM deployed cloud and precipitation radars, including the C-band Scanning ARM Precipitation Radar (CSAPR2), the X/Ka-band Scanning ARM Cloud Radar (SACR), and the Ka-band ARM Zenith cloud Radar (KAZR), as shown in Figure 1. This report presents an analysis of radar data quality, hardware calibrations, and radar data corrections.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗

NASA NTRS1994

A meteorological interpretation of the Arctic Boundary Layer Expedition (ABLE) 3B flight series

The Arctic Boundary Layer Expedition (ABLE) 3B was conducted to determine the summertime tropospheric distribution, sources, and sinks of important trace gas and aerosol species over the wetlands and boreal forests of central and eastern Canada. Isentropic trajectories and analyzed midtropospheric circulation patterns were used to group flights according to the transport histories of polar, midlatitude, or tropical air masses which were sampled. These data were then divided into bands of potential temperature levels representing the low, middle, and maximum aircraft altitudes to assess the effects of both local and long distance transport and natural and man-made pollutants to the measured chemical species. Detailed case studies are provided to depict the complex three-dimensional airflow regimes that transported air with differing chemical signatures to the study area. Mission 6 details the large-scale movement of smoke in the generally prevailing west to northwesterly airflow that was observed on the majority of flights. Mission 1 analyzes the horizontal and vertical motions of maritime Pacific air in the upper troposphere that was routinely mixed downward to the aircraft altitude. Finally, mission 14 tracks the far northward excursion of tropical air that had been associated with a Pacific typhoon. The following three factors all had important influences on the collected chemical data sets: (1) local and distant stratospheric in puts into the upper and middle troposphere; (2) biomass-burning plumes from active fires in Alaska and Canada; (3) a band of 'low ozone' upper tropospheric air that was observed by airborne differential absorption lidar (DIAL) above the aircraft maximum altitude. Other modification factors observed on some flights included urban pollution from U.S. and Canadian cities, tropical air that had been associated with a Pacific typhoon, and precipitation scavenging by clouds and rain. Many flights were affected by several of the above factors which led to complex chemical signatures that will be discussed in other companion papers.

Shipham, Mark C.↗

NASA NTRS2023

Characterization of Errors in Satellite-Based HCHO/NO 2 Tropospheric Column Ratios With Respect to Chemistry, Column-to-PBL Translation, Spatial Representation, and Retrieval Uncertainties

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2 ) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO 2 + RO 2 (LRO x ) to the chemical loss of NO x (LNO x ). Subsequently, we challenge the predictive power of HCHO/NO 2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LRO x /LNO x . FNRs show a strongly linear (R 2 =0.94) relationship with LRO x /LNO x , but only on the logarithmic scale. Following the baseline (i.e., ln(LRO x /LNO x ) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NO x -sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the RO x –HO x cycle. We calculate high ozone production rates (PO 3 ) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NO x -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km 2 ). A pixel with a grid size of 216 km 2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 molec. cm −2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Amir H. Souri↗